Industrial machinery, whether engines or power generating turbines, compressors, multipliers, etc. undergo unforeseen shutdowns and failures, often associated to aspects related to lubrication. The reduction in the service life of this industrial machinery often gives rise to unnecessary maintenance costs. Current ‘off-line’ measurement methodologies (oil sample analysis in the laboratory) do not provide a sufficiently early detection of the degradation process due to the low frequency with which these measurements are usually taken. Furthermore, in many contexts (transport, industrial, power . . . ) this control methodology entails a significant logistical and financial burden. To deal with this drawback, the idea is to develop a new generation of sensors capable of analysing the machine's condition in real time.
Critical machinery could benefit from an increase in reliability, reduction in maintenance costs and early problem identification through the use of smart sensors.
Lubricating oil is one of the key components in some of these machines and provides a lot of information regarding the machine's condition. Oil heating, for example, can be a sign that the machine is not operating in optimum conditions, and the presence of particles in the oil may indicate a future failure or considerable wear in the lubricated components. It could even point to the existence of cracks or faults in joints that could allow the entry of external contaminants.
Some of the parameters that it could be interesting to monitor in lubricating oil are as follows: particle determination (for example, quantification, classification of size or determination of shape), bubble content in the system or oil degradation based on colour. Below is a brief description of these parameters.
Particle determination in lubricated systems is a key aspect in many sectors and applications, since the particles provide information on the condition of the machine that is being monitored. In other words, the detection of particles in the oil is indicative in many cases of a situation that will generate a future failure or a breakdown in the machine, or the presence of a fault in filters or joints.
At present, most of these lubricated systems install filtering solutions that remove the particles from the lubrication system. However, filtering systems do not act on the root cause of the problem, and instead are limited to reducing the consequences of particle generation, whose presence in the lubrication system could accentuate the generation of more serious problems. At the same time, filtering systems present a series of limitations: they can become clogged or saturated, not being capable of removing any more particles.
Traditionally, laboratory techniques have been used in order to determine the quantity and type of wear particles present in lubricating oil, and also to classify them according to size. Subsequently, different technologies for on-line particle detection have started to emerge, such as:
Detectors through light blockage: These systems are based on the reduction in intensity that the detectors receive when a particle passes through the measuring cell to which a beam of light is supplied. No image is collected, instead this reduction in light is observed, principally on some wavelength. It is not possible to determine the shape of the particles but it is possible to determine their size. The main problem with these systems is the presence of water or air bubbles, which are counted as particles.
Detectors through pore blockage: They also use optical detection, but without image gathering. However, before this, the oil is made to pass through a mesh (10 micras approx.) for classification avoiding the presence of water and air bubbles at the time that the measurement is taken.
Magnetic/electric detectors: Sensors that use a magnetic principle to detect ferromagnetic particles on the fluid, by making the fluid pass through a magnetic field that is altered by the presence of the ferromagnetic particles.
Detectors through image analysis: These analyse an image, quantifying particle content according to size, shape and type, using a neural network algorithm. One would cite, for example, analysis equipment that uses an image capture system, together with a laser lighting system and powerful image processing software installed on a computer. It is also capable of identifying contaminants, free water, and fibres. The equipment quantifies wear particles having a size between 4-100 micras, and the shape of particles larger than 20 micras. Particle analysis in this range is useful for detecting mechanical faults in a wide variety of lubricated systems. Depending on the shape, the particle is classified as: a) cutting; b) fatigue; c) sliding; d) non-metallic.
Below is a mention of patents that disclose image-analysis detectors:
For example, U.S. Pat. No. 5,572,320 describes an image analysis detector that includes a lighting system based on a pulsed laser. Detection is carried out by means of a planar array of light sensitive photodiodes or phototransistors. However, the system of U.S. Pat. No. 5,572,320 is not capable of discriminating between particle shapes. Also, the measuring cell of U.S. Pat. No. 5,572,320 consists of a moving part that positions the oil in a specific place, and this complicates development and can be an important source of errors.
Meanwhile, U.S. Pat. No. 7,385,694B2 describes a detector through image analysis that includes a lighting system based on a pulsed laser and a camera for gathering images of the oil subjected to such lighting. However, the device of this patent does not allow a homogenous lighting to be provided over an inspection area that is greater than the beam of light itself. Also, the device requires a pump in order to pump the fluid to the measuring zone.
Another of the parameters that it could be interesting to monitor in lubricating oil is the bubble content in the system, since this can be indicative of foam generation in the oil and air retention in the system, which is undesirable. Both must be controlled and reduced to a maximum in order to achieve optimum functioning of the oil inside the system. This is critical in systems such as the multipliers of wind turbines. The maximum acceptable foam levels for used oil, according to method ASTM-D892, must not exceed:
TemperatureFormation (5′blowing)Stability (10′blowing)24° C.1001093.5° C.  2002024° C.10010
The content in retained oil must not exceed 25% in respect of new oil according to ASTM-D3427.
Finally, oil degradation based on colour is another parameter that may be interesting to monitor in lubricating oil:
Oil degradation is a key indicator of oil quality and how it fulfils its lubricating mission. It does not provide information on the machine directly, but indirectly from the speed of degradation it would be possible to extract information regarding the machine's operation. The degradation process of oil follows several very well-known steps: first it suffers a loss in additive content, then acidic compounds are generated, and finally, when it is in an advanced state of degradation, polymerisation processes begin in these acidic compounds that have been generated. The percentage of acidic constituents (in the form of additives in the case of new lubricating oils and in the form of oxidation compounds in the case of lubricating oils in service) is determined through analytical techniques.